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Record W2116831767 · doi:10.1158/1078-0432.ccr-09-1961

Approaches to Phase 1 Clinical Trial Design Focused on Safety, Efficiency, and Selected Patient Populations: A Report from the Clinical Trial Design Task Force of the National Cancer Institute Investigational Drug Steering Committee

2010· article· en· W2116831767 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Cancer Research · 2010
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Institutes of Health
KeywordsClinical trialMedicineClinical study designPopulationDrug developmentInvestigational DrugsPhases of clinical researchPatient safetyIntensive care medicineMedical physicsDrugOncologyPharmacologyInternal medicineHealth care

Abstract

fetched live from OpenAlex

The goals and objectives of phase 1 clinical trials are changing to include further evaluation of endpoints such as molecular targeted effects, in addition to dose-toxicity profile of the investigational agent. Because of these changes in focus, the National Cancer Institute and Investigational Drug Steering Committee's Task Force on Clinical Trial Design met to evaluate the most efficient ways to design and implement early clinical trials with novel therapeutics. Clinical approaches discussed included the conventional 3 + 3 cohort expansion phase 1 design, multi-institutional phase 1 studies, accelerated titration designs, continual reassessment methods, the study of specific target patient populations, and phase 0 studies. Each of these approaches uniquely contributes to some aspect of the phase 1 study, with all focused on dose and schedule determination, patient safety, and limited patient exposure to ineffective doses of investigational agent. The benefit of labor-intensive generation of preliminary biomarker evidence of target inhibition, as well as the value of molecular profiling of the study population, is considered. New drug development is expensive and the failure rate remains high. By identifying patient populations expected to respond to the study agent and tailoring the treatment with a novel drug, investigators will be one step closer to personalizing cancer treatment. The "fail early and fast" approach is acceptable if the appropriate patient population is evaluated in the phase 1 trial. The approaches outlined in this overview address the merits, advantages, disadvantages, and obstacles encountered during first in human studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.084
metaresearch head score (Gemma)0.568
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0840.568
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.946
GPT teacher head0.682
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it